The company brings a more than tenfold increase in efficiency to the components and systems used in the Global Automotive Electronics Market, estimated to reach $395 billion by 2024, with 70 percent of cars sold in 2025 expected to be connected cars.The data processing challenge of self-driving cars is illustrated by the fact it will generate 60,000X more data than the average smartphone today.

The company also announced that Infineon’s AURIX microcontrollers will be the first to market with an integration of Teraki’s breakthrough AI technology.

Teraki is taking technology honed at the highest end of data analytics accuracy requirements and scaling it efficiently for the highly-constrained automotive infrastructure and, over time, other data-intensive IoT markets.

Today, the automotive industry and its OEMs and insurance providers face an incredible opportunity to deliver innovative, cost-effective ways to use the vast amount of data generated by in-vehicle sensors, electronic control units (ECUs) and AI to improve vehicle safety and lower operational costs.

The problem, however, is that the high cost of expensive AI chips and the high computing demands of neural networks are preventing the widespread scaling of automotive AI applications. In addition, the limited processing power of ECUs, the bandwidth constraints of the in-vehicle CAN Bus, the data communication costs of car-to-cloud networks, and the time required to train AI and machine learning components have been significant barriers to developing and scaling new – and often real-time – applications.

Teraki vaults these barriers with its breakthrough, edge processing technology. Teraki down scales the cloud analytics models to fit and operate in or with resource- and cost-constrained automotive ECUs and networks. The result is more than a 4-10Xfactor increase in edge processing solutions for automotive chip and data communications and more than a 10X faster in AI or machine learning time performance.

“The biggest challenge for automotive system designers when implementing AI-driven applications is to find the balance between growing amounts of sensor data and the constraints of communication and processing technology. Utilizing Infineon’s AURIX microcontrollers that support ASIL-D systems, Teraki delivers an innovative approach that significantly improves data analytics and enables true low-latency mobility services,” said Ritesh Tyagi, head of the Infineon Silicon Valley Automotive Innovation Center. “For applications such as accident detection, driver behavior identification and predictive maintenance, the combination of these technologies translates into greater accuracy in detecting and responding to real-time events, resulting in higher levels of system reliability.”

Teraki has already generated significant momentum, completing several pre-production validations by premium automotive manufacturers and their chip suppliers, as well as having many ongoing proofs of concept with additional OEMs.

“The performance leaps our technology provides using Infineon’s AURIX microcontrollers usher in a new era of innovation possibilities for the automotive industry,” said Daniel Richart, cofounder and CEO of Teraki. “Our Intelligent Signal Processing software allows conventional sensors and ECUs to do far more, makes AI more practical, affordable and scalable, and significantly reduces CAN Bus and car-to-cloud bandwidth constraints.”

The mathematics behind the technology comes from co-founders Daniel Richart and Markus Kopf, and a talented team consisting of more than 10 researchers. Richart comes from the Max Planck Institute of Quantum Optics in Munich working under Nobel Prize-winning atomic physicist Theodor W. Hänsch. Richart led research projects in quantum computing, a new field challenged by analyzing enormous volumes of data representing the multiple possible simultaneous combinations of quantum states of a particle.